Dear Li Sun:

You appear to have a good deal of statistical confusion. R-help is not a
statistical consulting service. You should consult a local statistician or,
if that's not possible,post on a statistical help list like
stats.stackexchange.com

-- Bert

On Sun, Jun 24, 2012 at 12:04 PM, Li SUN <vraifreud.test@gmail.com> wrote:

> 2012/6/24 Uwe Ligges <ligges@statistik.tu-dortmund.de>:
> >
> >
> > On 24.06.2012 20:35, Li SUN wrote:
> >>
> >> Thanks David and Brian.
> >>
> >> But what if x is exact while y has some uncertainty Äy, in the
> >> relation y = k * x + b?
> >>
> >> Now I need to fit some data like
> >> x       = 1,          2,          3,          4,          5
> >> y±Äy = 1.1±0.1, 2.0±0.2, 3.1±0.2, 4.1±0.1, 5.0±0.2
> >>
> >> Is there any mechanism to pass x, y and Äy to lm() so that I can find
> >> k, b as well as their uncertainties Äk, Äb?
> >
> >
> > Again, no: this is not a linear model. Assumption in a linear model is
> that
> > the errors are identically distributed.
>
> Thanks, Uwe. Can the nonlinear model nls() handle this situation?
>
>
> >
> > Uwe Ligges
> >
> >
> >
> >
> >>
> >>
> >> Li Sun
> >>
> >>
> >> 2012/6/24 Prof Brian Ripley <ripley@stats.ox.ac.uk>:
> >>>
> >>> On 24/06/2012 18:39, David Winsemius wrote:
> >>>>
> >>>>
> >>>>
> >>>> On Jun 24, 2012, at 1:21 PM, Li SUN wrote:
> >>>>
> >>>>> Sorry for the confusion.
> >>>>>
> >>>>> Let me state the question again. I missed something in my original
> >>>>> statement.
> >>>>>
> >>>>> When using the linear model lm() to fit data of the form y = k * x +
> >>>>> b, where k, b are the coefficients to be found, and x is the variable
> >>>>> and has an error bar (uncertainty) Äx of the same length associated
> >>>>> with it. Is it possible to pass Äx to the linear model lm(), and from
> >>>>> the output to find the uncertainty Äk for k, Äb for b as well?
> >>>>
> >>>>
> >>>>
> >>>> In one sense this could be done if you were interpreting the "Äx" as
> the
> >>>> vector of individual residuals of a model, but I'm guessing that might
> >>>> not be what you meant. You would be able to recover the original data,
> >>>> assuming you knew the X values, and would proceed by calculating the Y
> >>>> values as the sum of predictions and the residuals, thus recovering
> the
> >>>> original data. But  I'm guessing you want to supply a small number of
> >>>> parameters from an analysis you are reading about and you are hoping
> to
> >>>> be getting from lm() further information to answer some question.
> That's
> >>>> not the direction of teh flow of information. The flow is data INTO
> >>>> lm(), estimation of parameters OUT.
> >>>>
> >>>> Show us a sample dataset constructed with R code or show us the
> console
> >>>> output of dput() applied to your dataset, and you may get better
> answers
> >>>> to what is still an unclear question.
> >>>>
> >>>
> >>> This is not linear regression if 'x' is not known exactly.  There are
> >>> various formulations of the problem, but that is off-topic here.
> However,
> >>> consulting
> >>>
> >>> @Book{Fuller.87,
> >>>  author       = "Fuller, Wayne A.",
> >>>  title        = "Measurement Error Models",
> >>>  publisher    = "John Wiley and Sons",
> >>>  address =      "New York",
> >>>  year         = "1987",
> >>>  ISBN         = "0-471-86187-1",
> >>> }
> >>>
> >>> would be a good start.
> >>>
> >>> --
> >>> Brian D. Ripley,                  ripley@stats.ox.ac.uk
> >>> Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
> >>> University of Oxford,             Tel:  +44 1865 272861 (self)
> >>> 1 South Parks Road,                     +44 1865 272866 (PA)
> >>> Oxford OX1 3TG, UK                Fax:  +44 1865 272595
> >>>
> >>>
> >>> ______________________________________________
> >>> R-help@r-project.org mailing list
> >>> https://stat.ethz.ch/mailman/listinfo/r-help
> >>> PLEASE do read the posting guide
> >>> http://www.R-project.org/posting-guide.html
> >>> and provide commented, minimal, self-contained, reproducible code.
> >>
> >>
> >> ______________________________________________
> >> R-help@r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-help
> >> PLEASE do read the posting guide
> >> http://www.R-project.org/posting-guide.html
> >> and provide commented, minimal, self-contained, reproducible code.
> >>
> >
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>



-- 

Bert Gunter
Genentech Nonclinical Biostatistics

Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm

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